Abstract 14: Effects of Lesion Laterality on Post-Stroke Motor Performance: An ENIGMA Stroke Recovery Analysis
Bibliographic record
Abstract
The laterality of the lesioned hemisphere is often overlooked in stroke recovery research due to small sample sizes. Here, we used a well-powered dataset from ENIGMA Stroke Recovery (a consortium that harmonizes post-stroke MRIs and behavioral data worldwide; http://enigma.usc.edu) to analyze the effects of left (LHL) versus right (RHL) hemisphere lesions on motor performance. Given the different functional roles of each hemisphere, we hypothesized that the LHL group should show better motor performance, and, consequently, different brain-behavior relationships, compared to the RHL group. Data from over 2000 stroke patients across 20 sites worldwide has been committed. To date, structural T1-weighted MRIs from n=343 (10 sites) have been analyzed (LHL n=174; RHL n=169). ENIGMA protocols extracted volumes of subcortical regions of interest and provided quality control. Regression analyses examined brain volumes as predictors of motor outcomes. Motor scores were combined across scales/sites, with each score expressed as a percentage of the maximum score. Covariates (e.g., age, sex, intracranial volume) and manually marked lesion effects were also modeled. Statistical significance was assessed nonparametrically by permutation. As anticipated, the LHL group had better motor performance compared to the RHL group (t(1,341)=3.07, p=0.0023). In addition, while the combined LHL+RHL analyses showed significant associations between motor scores and volumes of the basal ganglia/lateral ventricles, separate group analyses showed strong associations for the LHL group, but only one association for the RHL group (Table 1). This may suggest that motor recovery following RH lesions is more heterogeneous or relies more on cortical regions/networks that were not assessed here. While further research is needed, these results suggest that laterality of the lesioned hemisphere affects neural patterns related to motor recovery and should be carefully examined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".